Future prospective, multi-center, large-scale cohort studies that incor- porate serial dynamic biomarkers and detailed procedural and treat- ment data are warranted to validate, refine, and enhance th

The gap

Consensus across the literature

Supporting evidence — 6 representative gaps

• Traditional statistics and artificial intelligence-based prognostic models for predicting type 2 diabetes mellitus after gestational diabetes: a systematic review (2026) · doi

  A strength is that we included all available prognostic models on T2D following GDM, regardless of the set- ting and modelling methods. We summarise character- istics of developed or evaluated models, with particular focus on calibration, overall measure, and clinical utility measures - areas not addressed in earlier reviews [30, 33]. Additionally, this review is the first to apply up-to- date best-practice guides such as the PROBAST + AI for the prediction field to assess regression- and machine learning-based prognostic models. We did not undertake meta-analysis because experts agree that this is inappro- priate when less than five external validation studies not available and assumptions violate for pooling [10, 29]. Meta-analysis when done on inappropriately selected or non-comparable samples enables inappropriate compari- sons to be made, thereby not assisting the discipline to progress using the correct available evidence. Optimistic but non-generalisable performance of the models due to high concern for applicability might arise from overfitting risk due to imbalance of events per pre- dictor, validation choice, missing data handling, and underreporting of key calibration and clinical utility measures. Specifying the exact gestational week would strengthen clarity, however, the original studies included in our review typically reported BMI as measured in “early pregnancy” or “at the start of pregnancy” without providing a specific gestational age. As such, we were unable to extract an exact gestational week. In general, early-pregnancy BMI is commonly assessed during the first trimester, typically around or before 12 weeks’ ges- tation, consistent with how this is defined in the litera- ture [73]. Although planned, we were unable to conduct a meta-analysis of performance metrics due to the lack of external validations and considerable heterogeneity between the studies. No examples of models integrating Yimer et al. Diagnostic and Prognostic Research (2026) 10:12 AI and ML with causal inference approaches were found, however, these new approaches have been proposed to enhance interpretability and warrant future research.

  Keywords: models available prognostic meta gestational pregnancy included calibration clinical utility measures review first external validation
• Presepsin and procalcitonin kinetics for sepsis early risk stratification: A multicenter clinical chemistry study (2026) · doi

  Several limitations should be considered in reading these findings. First, the inherent heterogeneity in the onset of symptoms and pre-enrollment treatment (including prior use of antibiotics) in the outpatient pathway can lead to poorer performance of biomarkers on baseline in terms of trait differences and difficulties when comparing biomarkers between participants. Second, kinetics analyses rely on follow-up sampling taken at 24 and 48 hours; it may be difficult to practically achieve complete follow-up, and in their absence selection bias may occur if participants who are missing at timepoints during follow-up have different characteristics with regard to severity, referral or access to care. Third, a combination of a range of ages from children to adults means it is necessary to include age-related sepsis definitions and severity assessment; this means incomplete organ dysfunction variables have implications for classification and comparability between strata. Fourth, the interpretation of presepsin is affected by renal function; hence, residual confounding occurs despite adjusting and planned stratification. Fifth, the sample size may be too small to reliably model more than a few predictors of 28-day mortality, and this could increase the risks of model optimism without rigorous internal validation and later external validation.

  Keywords: follow biomarkers participants severity means model validation several limitations considered reading first inherent heterogeneity onset
• Building and validating machine learning models to predict appendiceal perforation during conservative treatment of fecalith-associated appendicitis: a 20-algorithm multicenter retrospective analysis (2026) · doi

  External validation in multi-center cohorts represents the next critical step for clinical implementation47. Integration of additional data sources, such as continuous vital sign monitoring or novel biomarkers, may further enhance predictive accuracy49. Real-time implementation studies will be essential to evaluate clinical impact, workflow integration, and cost-effectiveness. Regarding deployment format, we envisage the Gradient Boosting model being made available as a web-based risk calculator (planned deployment via a dedicated URL) allowing clinicians to enter the eight input variables at the bedside. Prospective real-time implementation studies will be essential to evaluate clinical impact, workflow integration, and cost-effectiveness50. Development of a simplified scoring chart derived from the model, while sacrificing some predictive accuracy, may improve adoption in settings without electronic clinical decision support51. Investigation of optimal intervention strategies for high-risk patients identified by these models represents an important clinical question. Whether enhanced monitoring, alternative antibiotic regimens, or earlier surgical intervention improves outcomes requires prospective evaluation52.

  Keywords: clinical implementation integration represents monitoring predictive accuracy real time essential evaluate impact workflow cost effectiveness
• Leveraging dynamic serum uric acid trajectories for risk stratification in hospitalized HFpEF patients (2026) · doi

  Several limitations of this study should be acknowledged. First, this is an observational study; despite rigorous multivariable adjustment and sensitivity analyses, causality cannot be inferred. All findings and interpreted hypothesis-generating. associational should be as Second, our trajectory classification was based on only two SUA measurements (admission and pre-discharge). This simplification may not fully capture complex in-hospital fluctuations (e.g., multiple peaks or nadirs) and may miss dynamic patterns that require three or more serial measurements. Therefore, our trajectory groups represent a prag- matic approximation rather than a true longitudinal characterization. Third, the optimal SUA cut-off for MACE prediction (5.6 mg/dL) was derived from this cohort and has not been externally validated. Its generalizability to other populations or settings is uncertain. We there- fore suggest that SUA should be used in combination with other clini- cal markers (e.g., BNP, eGFR, age) for risk stratification, rather than as a standalone test. Fourth, our analysis did not adjust for several potential con- founders due to data unavailability, including: in-hospital loop diuretic dose intensity (cumulative intravenous/oral doses could not be reliably standardized), post-discharge medication

  Keywords: several trajectory measurements discharge hospital rather limitations acknowledged first observational despite rigorous multivariable adjustment sensitivity
• Analysis of risk factors for heart failure in patients with type 2 diabetes mellitus and acute ST-segment elevation myocardial infarction after percutaneous coronary intervention (2026) · doi

  Future prospective, multi-center, large-scale cohort studies that incor- porate serial dynamic biomarkers and detailed procedural and treat- ment data are warranted to validate, refine, and enhance the clinical applicability of this prediction model. Future prospective, multi- center, large-scale cohort studies that incorporate serial dynamic biomarkers, detailed procedural and treatment data, and independent external validation are warranted to validate, refine, and enhance the clinical applicability of this prediction model.

  Keywords: future prospective multi center large scale cohort serial dynamic biomarkers detailed procedural warranted validate refine
• Clinical Prognostic Scoring Systems in Heart Failure with Preserved Ejection Fraction: An Integrative Review of Risk Prediction Models (2026) · doi

  This review is narrative and integrative rather than a preregistered systematic review, and we did not pool estimates. The main limitation is heterogeneity across the underlying studies. Cohorts differed by setting (hospitalized, post-discharge, outpatient, trial), HFpEF definitions and diagnostic work-up, phenotype distributions, and endpoints. As a result, numerical performance metrics are not fully comparable, and transportability across contexts is constrained. Reporting was inconsistent. Discrimination was frequently provided, whereas calibration was variably assessed and rarely standardized. Effect measures were reported on different scales, and robust external validation and within-cohort head- to-head comparisons were limited. In addition, several tools discussed as prognostic tools (e.g., H2FPEF and HFA-PEFF) were originally designed for diagnostic evaluation; therefore, prognostic use represents off-label repurposing and may be sensitive to missing components and local phenotype mix. Finally, the focus on clinician-usable scores may underrepresent higher-dimensional models that 70 A. Draghici and G.-A. Dan 9 could improve prediction but require infrastructure and recalibration before routine implementation. CONCLUSIONS Baseline clinical scores in HFpEF offer only moderate discrimination. Performance appears to improve when assessment reflects what drives events, such as residual congestion, nutritional or inflammatory state, and how these change over time. A layered workflow seems most practical: start with an implementable clinical or diagnostic score (MAGGIC, GWTG-HF, H2FPEF/HFA-PEFF), add natriuretic peptides, check discharge lung ultrasound for B-lines, and follow simple nutrition indices plus KCCQ longitudinally. This combination is feasible, low-cost, and suitable for routine clinical use. It may support admission triage, early post-discharge planning, and follow-up. Utility is likely phenotype-dependent: nutrition-centric indices are particularly informative in older or frail patients, while WATCH-DM provides diabetes- specific stratification. Figure 1. Layered risk reassessment aligned to clinical decision points. Introducere: Stratificarea riscului în insuficiența cardiacă cu fracție de ejecție păstrată (HFpEF) rămâne neuniformă în practica curentă, în ciuda existenței multiplelor scoruri prognostice. Persistă controverse importante. Una dintre acestea privește utilizarea scorurilor largi, derivate clinic, comparativ cu instrumentele fundamentate pe mecanisme fiziopatologice, inclusiv cadrele diagnostice utilizate pragmatic în scop prognostic. O altă controversă vizează distincția dintre riscul static, evaluat la momentul inițial, și stările de risc dinamice, care se modifică pe parcursul spitalizării și în pe

  Keywords: clinical discharge hfpef diagnostic phenotype prognostic review across post performance discrimination head tools fpef peff